Soil moisture represents many attributes of the geo-hydrological cycle and the climate system. Citizen science through social media as an emerging tool could be utilized to collect soil moisture data. A pilot study area was selected in Shahriar, Iran. A user interface and a sampling process (use of citizen science by subscribers) were designed to analyze the subjective and gravimetric soil moisture data. Furthermore, explanatory moisture condition (EMC), a new initiative to consider land use in soil moisture information from vegetation cover, was evaluated. A statistical artificial neural network was used for quantifying subjective data, and soil moisture layouts were produced by utilizing the ordinary kriging (OK) method. For cross-validating, the land surface temperature data from the MODIS satellite were retrieved. A platform for the region with 200 m grids resolution to collect daily soil moisture at eight ungauged stations is proposed to utilize subjective data from the subscribers and cross-validated with satellite data. A virtual station at the centroid of the pervious part of the study area was selected as a reference station for data collection daily or weekly to generate soil moisture time series. The results showed a high potential of utilizing satellite and citizen science data for real-time estimation of scarce soil moisture data in developing regions.

土壤水分代表了地质水文循环和气候系统的许多属性。通过社交媒体作为新兴工具的公民科学可用于收集土壤水分数据。在伊朗的 Shahriar 选择了一个试点研究区域。用户界面和采样过程（订阅者使用公民科学）旨在分析主观和重力土壤水分数据。此外，还对解释性水分条件 (EMC) 进行了评估，这是一项在来自植被覆盖的土壤水分信息中考虑土地利用的新举措。统计人工神经网络用于量化主观数据，并利用普通克里金（OK）方法生成土壤水分布局。为了进行交叉验证，检索了来自 MODIS 卫星的地表温度数据。提出了一个具有 200 m 网格分辨率的区域平台，用于在八个未测量的站点收集每日土壤水分，以利用来自订户的主观数据并与卫星数据进行交叉验证。选择研究区透水部分质心的虚拟站作为参考站，每天或每周收集数据以生成土壤水分时间序列。结果表明，利用卫星和公民科学数据实时估算发展中地区稀缺的土壤水分数据具有很大潜力。选择研究区透水部分质心的虚拟站作为参考站，每天或每周收集数据以生成土壤水分时间序列。结果表明，利用卫星和公民科学数据实时估算发展中地区稀缺的土壤水分数据具有很大潜力。选择研究区透水部分质心的虚拟站作为参考站，每天或每周收集数据以生成土壤水分时间序列。结果表明，利用卫星和公民科学数据实时估算发展中地区稀缺的土壤水分数据具有很大潜力。